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Treatment Effect Estimation via Differentiated Confounder Balancing and Regression by Kun Kuang; Peng Cui; Bo Li; Meng Jiang; Yashen Wang; Fei Wu; Shiqiang Yang is a Computer Science article available to read on EtoBox.

What is Treatment Effect Estimation via Differentiated Confounder Balancing and Regression about?

Treatment effect plays an important role on decision making in many fields, such as social marketing, healthcare, and public policy. The key challenge on estimating treatment effect in the wild observational studies is to handle confounding bias induced by imbalance of the confounder distributions between treated and control units. Traditional methods remove confounding bias by re-weighting units with supposedly accurate propensity score estimation under the unconfoundedness assumption. Controlling high-dimensional variables may make the unconfoundedness assumption more plausible, but poses new challenge on accurate propensity score estimation. One strand of recent literature seeks to directly optimize weights to balance confounder distributions, bypassing propensity score estimation. But existing balancing methods fail to do selection and differentiation among the pool of a large number of potential confounders, leading to possible underperformance in many high-dimensional settings. In this article, we propose a data-driven Differentiated Confounder Balancing (DCB) algorithm to jointly select confounders, differentiate weights of confounders and balance confounder distributions fo

Who reads Treatment Effect Estimation via Differentiated Confounder Balancing and Regression?

Author

Kun Kuang; Peng Cui; Bo Li; Meng Jiang; Yashen Wang; Fei Wu; Shiqiang Yang

Publisher

ACM

Published

2019

Language

EN

Field

Computer Science (Physical Sciences)